My slides

• Before we get into it, I have put all of my material on the web
  
  

• You can find my slides (along with copy-pasteable code) on my GitHub repository:

  • https://github.com/JoshMerfeld/geospatialdataR
  • Scroll down to the bottom and you’ll find all of the links you’ll need.
  • I’d suggest you have this page open during classes. I’ll sometimes ask you to use something on the repo.

Let’s get started!

• Let’s get started with the hands-on session.

• As a first step, I’d like to hear from all of you:

  • How much experience do you have using R?
  • This is all experience, not just with SAE

Some things to note

• We will be using RStudio throughout the workshops
  • There are other options you are welcome to use (VS Code is the most common alternative)
• Two general “data cleaning” pipelines:
  • The tidyverse
  • The data.table package (which builds on base R)
• We will be using the tidyverse

Getting started with RStudio

• Let’s start by looking at the layout of RStudio.

• For those of you with ample R experience, nothing here will be new!

Why don’t you all give it a try

• Create a script in RStudio

• Save that script in a specific place (folder) on your computer

  • Make sure to keep track of where you save it!
  • I create a folder for each specific project I work on
  • e.g. you could create “Nairobi Workshops” and save the script as “day1.R”

First things first: the working directory

• The working directory is the folder that R is currently working in
  • This is where R will look for files
  • This is where R will save files
  • This is where R will create files
• You can always write out an entire file path, but this is tedious
  • More importantly, it makes your code less reproducible since the path is specific to YOUR computer

First things first: the working directory

• One nice thing about R is that the working directory will automatically be where you open the script from
  • Let’s try this. Save your script to a folder on your computer, then open the script from that folder.
  • Let’s see if it worked!

getwd() # this command will show you your current working directory

[1] "/Users/Josh/Dropbox/KDIS/Classes/geospatialdataR"

First things first: the working directory

• You can also set the working directory in RStudio
  • Session > Set Working Directory > Choose Directory (or Source File Location)
  • Give it a try and let’s see if it worked!

getwd() # this command will show you your current working directory

[1] "/Users/Josh/Dropbox/KDIS/Classes/geospatialdataR"

Always use the same working directory!

• Make sure to always set the working directory to the same location when working in the same script!
  
  

• This will avoid problems later

  • It also makes your code more reproducible (e.g. if a colleague wants to run it, you just send the entire folder and it works with no changes)

R packages

• R is a language that is built on packages
  • Packages are collections of functions that do specific things
  • R comes with a set of “base” packages that are installed automatically
• We are going to use one package consistently, called the “tidyverse”
  • This consists of a set of packages that are designed to work together, with data cleaning in mind

R packages

The one exception to always using a script? I install packages in the CONSOLE. You can install packages like this:

install.packages("tidyverse") # this will install the tidyverse package. Note the quotes!

• You only need to install a package once on your computer.

R packages

The first thing you’ll do in your script is load packages. You do it like this:

'''
This script is part of the Nairobi Workshop on SAE.
Date: 26 August 2024 (written earlier!)
Author: Josh Merfeld
'''
# Load packages (libraries)
library(tidyverse)

• Note that the first part is a comment I’ve added to the script.
  • I make a lot of comments!